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  • Jennifer M Mueller-Phillips
    Design and Evaluation of a Continuous Data Level Auditing...
    research summary posted February 15, 2015 by Jennifer M Mueller-Phillips, tagged 08.0 Auditing Procedures – Nature, Timing and Extent, 08.01 Substantive Analytical Review – Effectiveness, 08.08 Projecting Interim Testing Conclusions Year End, 08.09 Impact of Technology on Audit Procedures 
    Title:
    Design and Evaluation of a Continuous Data Level Auditing System
    Practical Implications:

    This paper is intended to prompt auditors to take advantage of easier access to population data in today’s digital business environment. By abandoning sampling auditors can develop much more sophisticated models of behavior that can identify anomalies in ways that were not possible before. Auditors can also be more creative in how they treat data, be it in aggregating it across organizational subunits or in larger and smaller time units. Most innovative of all, auditors and/or managers have the ability to continually update their expectation models by investigating errors and anomalies in real time and correcting them, so that the model is not based on flawed data. We find that such error correction greatly improves the accuracy of analytical procedures. Perhaps the most important finding, however, is that almost all the various expectation models we used gave similarly strong results which implies that what really matters is the size of the data set. Once auditors move away from sampling they will find that population data provides great statistical power when developing analytical procedures that reduces the reliance on finding just the right such procedure.

    For more information on this study, please contact Alexander Kogan.

    Citation:

    Kogan, A., M. Alles, M. Vasarhelyi and J. Wu. 2014. Design and Evaluation of a Continuous Data Level Auditing System. Auditing: A Journal of Practice and Theory. 33 (4): 221-245.

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